基于灰度阈值自优化模型的球墨铸铁珠光体数量评定

Quantity Evaluation of Pearlite in Nodular Cast Iron Based on Self-Optimization Model for Gray Threshold

以球墨铸铁为对象,通过图像灰度处理与二值化处理、珠光体与石墨连通域逐个提取、各区域灰度均值求解、灰度阈值自优化模型建立、珠光体数量分级与评定的技术路径,实现金相显微图片中铁素体区域、珠光体区域与石墨区域的有效分离。以某型号柴油机机身铸件材料为对象,验证珠光体数量自动评定方法的正确性。解决人工评定的主观性问题,同时显著提升金相检测的效率。

In order to separate the ferrite,pearlite and graphite regions in the metallographic micrograph effectively,research was carried out on the nodular cast iron.The gray processing and binarization processing was carried out,the connected regions of pearlite and graphite was extracted one by one,the mean gray values of the regions was solved,the self-optimization model for gray threshold was established.On this basis,the quantity of pearlite in nodular cast iron could be graded and evaluated.The automatic evaluation method for the pearlite quantity was verified by taking the casting material of a diesel engine as an instance.This method could avoid the subjectivity of manual evaluation and improve the efficiency of metallographic examination remarkably.